Confidence Intervals for the Signal-to-Noise Ratio and Difference of Signal-to-Noise Ratios of Log-Normal Distributions

Thangjai, Warisa; Niwitpong, Sa-Aat

doi:10.3390/stats2010012

Cited by 14 publications

(14 citation statements)

References 18 publications

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“…The coefficient of variation is lacking the unit of measurement and is useful in comparing the variability between observation groups. Many researchers have proposed confidence intervals for the coefficient of variation [22]. Other parameters are the skewness and kurtosis coefficients described in the diagram illustrated in Figure 2.…”

Section: Standalone Modelsmentioning

confidence: 99%

Intelligent Writing System for Patients with Upper Limb Disabilities

et al. 2022

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Subjects with neurological problems and neurological disorders can be recovered in the dedicated recovery centers and at home. The paper presents a smart writing system that uses dedicated movements to recover specific problems of the hands. The system is based on a smart pen equipped with vibration, acceleration, and gyroscope sensors that give remote data to a computer via an ESP Arduino board via Bluetooth. The purpose of the system design is to serve in applications such as recognizing the writing, drawings, or emotional and physiological states of the writer. The data is preprocessed by extracting the characteristics. They will be used in a prediction and classification system using machine learning (ML) algorithms. The paper proposes a new method of using the dynamic processing of certain geometric shapes that are recognized and analyzed to diagnose the mobility of the subjects’ hands. Using methods of clustering data as K-means and classification as support vector machine (SVM), the results obtained from the data analysis using the TensorFlow platform have an accuracy of over 70%. Due to the adaptation algorithms used, the system can be customized, learning the hand movement of the subject.

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Section: Standalone Modelsmentioning

confidence: 99%

Intelligent Writing System for Patients with Upper Limb Disabilities

et al. 2022

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“…Thangjai et al [10], Nam and Kwon [11], Hasan and Krishnamoorthy [12], Niwitpong et al [13], Thangjai and Niwitpong [14], and Thangjai et al [15] suggested confidence intervals for functions of the coefficient of variation of a log-normal distribution. Furthermore, Thangjai and Niwitpong [16,17] introduced approaches to estimate the confidence intervals for functions of the signal-to-noise ratio of log-normal distributions.…”

Section: -Introductionmentioning

confidence: 99%

Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion

Thangjai

Niwitpong

2021

Emerg Sci J

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Herein, we propose the Bayesian approach for constructing the confidence intervals for both the coefficient of variation of a log-normal distribution and the difference between the coefficients of variation of two log-normal distributions. For the first case, the Bayesian approach was compared with large-sample, Chi-squared, and approximate fiducial approaches via Monte Carlo simulation. For the second case, the Bayesian approach was compared with the method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using Monte Carlo simulation. The results show that the Bayesian approach provided the best approach for constructing the confidence intervals for both the coefficient of variation of a log-normal distribution and the difference between the coefficients of variation of two log-normal distributions. To illustrate the performances of the confidence limit construction approaches with real data, they were applied to analyze real PM10 datasets from the Nan and Chiang Mai provinces in Thailand, the results of which are in agreement with the simulation results. Doi: 10.28991/esj-2021-01264 Full Text: PDF

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“…Meanwhile, several authors have discussed which parameter should be used in statistical inference for a log-normal distribution (Lacey et al, 1997;Royston, 2001;Krishnamoorthy and Mathew, 2003;Hannig et al, 2006;Tian and Wu, 2007;Sharma and Singh, 2010;Harvey and van der Merwe, 2012;Lin and Wang, 2013;Rao and D'Cunha, 2016;Thangjai et al, 2016;Nam and Kwon, 2017;Hasan and Krishnamoorthy, 2017;Thangjai and Niwitpong, 2019). Furthermore, the coefficient of variation has been used in various applications (Tsim et al, 1991;Faupel-Badger et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation

Thangjai

Niwitpong

2020

Statistics in Transition New Series

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Recently, harmful levels of air pollution have been detected in many provinces of Thailand. Particulate matter (PM) contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. A high dispersion of PM is measured by a coefficient of variation of log-normal distribution. Since the log-normal distribution is often used to analyse environmental data such as hazardous dust particle levels and daily rainfall data. These data focus the statistical inference on the coefficient of variation. In this paper, we develop confidence interval estimation for the ratio of coefficients of variation of two log-normal distributions constructed using the Bayesian approach. These confidence intervals were then compared with the existing approaches: method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using their coverage probabilities and average lengths via Monte Carlo simulation. The simulation results show that the Bayesian confidence interval performed better than the others in terms of coverage probability and average length. The proposed approach and the existing approaches are illustrated using examples from data set PM10 level and PM2.5 level in the northern Thailand.

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Confidence Intervals for the Signal-to-Noise Ratio and Difference of Signal-to-Noise Ratios of Log-Normal Distributions

Cited by 14 publications

References 18 publications

Intelligent Writing System for Patients with Upper Limb Disabilities

Intelligent Writing System for Patients with Upper Limb Disabilities

Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion

Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation

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